The present disclosure relates generally to building management systems. The present disclosure relates more particularly to a building management system which identifies steady states for a process operated by equipment connected to the building management system.
A building management system (BMS) is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area. A BMS can include, for example, a HVAC system, a security system, a lighting system, a fire alerting system, any other system that is capable of managing building functions or devices, or any combination thereof.
Systems and devices in a BMS often generate temporal (i.e., time-series) data that can be analyzed to determine the performance of the BMS and the various components thereof. From the data generated by the BMS, it can be detected whether the operation of various components (e.g., chiller, rooftop units, air handling units, variable refrigerant flow system, etc.) is in a steady state or transient state (e.g., a transition state). As used herein, a steady state refers to a state in which one or more variables associated with the operation of a component or a system are substantially unchanging or changing very slightly in time. A transient state refers to a state in which one or more variables associated with the operation of a component or a system are changing considerably in time and have not reached the steady state. Steady state identification can be useful in various applications. For example, the BMS may use only the temporal data generated during the steady state, but not use the temporal data generated during the transient state, for detecting, diagnosing, and/or predicting the system operation to avoid false detection, diagnosis, and/or prediction. In particular, the BMS may include various modelers that use temporal data generated during the steady state for estimating parameters in first principle models, estimating coefficients in regression models, calculating mass and energy balances, building principal component analysis (PCA) models, building partial least squares (PLS) models, and so on.
Typically, methods for steady state detection address a single variable associated with the operation process at a time, which can be expanded to multiple variables by applying to each of the variables independently. Furthermore, typical methods may need to tune several parameters in order to work properly. As such, these methods may be unaffordable given the minimal operational supervision and the low-cost nature of the industry (e.g., the HVAC industry). It would be desirable to have a system and method for steady state detection that provides reliable results in the situation of multiple variables at a low cost, and is robust to various types of data.